Data Science Techniques in Knowledge-Intensive Business Processes: A Collection of Use Cases for Investment Banking

Foundations

Well-founded methods, processes and algorithms as well as supporting systems are already being used in many companies to generate knowledge from data. Various concrete techniques such as neural networks and expert systems are discussed in science and practice (Gallant, 1993). In general, such techniques aim to support a company's strategy, structures, and processes so that lessons learned can be used to improve routine tasks (incremental innovation) or even to overcome with completely new business models (radical or disruptive innovations) (Vyas, 2016). By using data with digital techniques, knowledge can be codified and shared between employees, teams and even virtual agents. In a nutshell, with digital data science techniques, organizations can create new value and innovation through knowledge (Berman, 2018).The particular relevance of the topic of data science is reflected by the fact that it is not the pure acquisition of information that is at the center of attraction - due to globalization and the associated rapid growth of digital networks, smart business decisions are characterized by enormous volume, speed and heterogeneity of data (sometimes discussed under the term “big data”). The main task for many companies improving their processes data-driven is to use internal as well as external knowledge to make better decisions in order to increase a company's learning, adaptability and innovation capacity (Stairs & Reynolds, 2017).